Training Set & Validation Set¶
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from pathlib import Path
from keras.preprocessing import image
from keras.applications import Xception, xception, InceptionV3, inception_v3, InceptionResNetV2, inception_resnet_v2, NASNetLarge, nasnet
import numpy as np
import pandas as pd
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
KTF.set_session(tf.Session(config=tf.ConfigProto(device_count={'gpu':0})))
import os
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
FOLDER_TRN = Path('train/')
FOLDER_TEST = Path('test/')
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df = pd.read_csv('data_clean.csv')
df.head()
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from sklearn.model_selection import train_test_split
# Dataframe of training set & validation set
df_trn, df_val = train_test_split(df, stratify=df['label'], random_state=0)
df_trn.reset_index(drop=True, inplace=True)
df_val.reset_index(drop=True, inplace=True)
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def show_images_from_df(data_df, nrows, ncols, directory=FOLDER_TRN, is_train=True, pred_label=None):
"""
showing images from dataframe.
"""
fig, ax = plt.subplots(nrows, ncols, figsize=(15, 15))
plt.subplots_adjust(wspace=.4, hspace=.5)
for idx, row in data_df[:nrows*ncols].iterrows():
fname = row['file']
prob = pred_label[idx] if not is_train else 1.000
label = row['label'] if is_train else ('dog' if prob > 0.500 else 'cat')
img = plt.imread(os.path.join(directory, fname))
ax[idx // ncols, idx % ncols].imshow(img)
if is_train:
ax[idx // ncols, idx % ncols].set_title("file: {}\n label: {}".format(fname, label), size=12)
else:
ax[idx // ncols, idx % ncols].set_title("file: {}\n prediction: {}\nprobability:{:.3f}".format(fname, label, prob), size=12)
ax[idx // ncols, idx % ncols].get_xaxis().set_visible(False)
ax[idx // ncols, idx % ncols].get_yaxis().set_visible(False)
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# Show images of training data
show_images_from_df(df_trn, 5, 5)
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# Show images of validation data
show_images_from_df(df_val, 5, 5)
Data Augmentation¶
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from keras.preprocessing.image import ImageDataGenerator
# Batch size
BATCH_SIZE_331 = 16
BATCH_SIZE_299 = 32
# Image size
SIZE_299 = (299, 299)
SIZE_331 = (331, 331)
def generator_flow(
datagen, df, target_size, batch_size,
directory=FOLDER_TRN, x_col='file', y_col='label',
mode='binary', shuffle=True
):
gen = datagen.flow_from_dataframe(
df,
directory=directory,
x_col=x_col,
y_col=y_col,
target_size=target_size,
class_mode=mode,
batch_size=batch_size,
shuffle=shuffle
)
return gen
datagen_trn = ImageDataGenerator(
rotation_range=25,
width_shift_range=.1,
height_shift_range=.1,
zoom_range=.2,
shear_range=.1,
horizontal_flip=True
)
datagen_val = ImageDataGenerator()
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# Data generator of training data & validation data with different size
generator_trn_331 = generator_flow(datagen_trn, df_trn, SIZE_331, BATCH_SIZE_331)
generator_val_331 = generator_flow(datagen_val, df_val, SIZE_331, BATCH_SIZE_331)
generator_trn_299 = generator_flow(datagen_trn, df_trn, SIZE_299, BATCH_SIZE_299)
generator_val_299 = generator_flow(datagen_val, df_val, SIZE_299, BATCH_SIZE_299)
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# Class indices
print(generator_trn_331.class_indices)
print(generator_trn_299.class_indices)
print(generator_val_331.class_indices)
print(generator_val_299.class_indices)
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# define a function for sorting the testing images in their directory by number in image name
import re
def key_func(entry):
"""
sort files in their directory by number of its name.
"""
return int(re.search(r'\d+', entry).group())
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# Sort testing data
fnames_tst = os.listdir(FOLDER_TEST)
fnames_tst.sort(key=key_func)
fnames_tst[:10]
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# Generator for testing data, set 'shuffle'=False
df_tst = pd.DataFrame({
'file': fnames_tst
})
datagen_tst = ImageDataGenerator()
generator_tst_331 = generator_flow(datagen_tst, df_tst, (331, 331), 16, directory=FOLDER_TEST, y_col=None, mode=None, shuffle=False)
generator_tst_299 = generator_flow(datagen_tst, df_tst, SIZE_299, BATCH_SIZE_299, directory=FOLDER_TEST, y_col=None, mode=None, shuffle=False)
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from keras.models import Model
from keras.layers import Input, Lambda
SHAPE_299 = (299, 299, 3)
SHAPE_331 = (331, 331, 3)
def build_pretrained_model(base_model, input_shape, preprocess_func, weight_scheme='imagenet', trainable=False):
"""
pretrained model, remove top level, preprocess inputs, for extrating features.
"""
model_input = Input(shape=input_shape)
tensor = Lambda(preprocess_func)(model_input)
pretrained_model = base_model(input_tensor=tensor, weights=weight_scheme, include_top=False, pooling='avg')
model = Model(model_input, pretrained_model.output)
model.trainable = trainable
return model
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# Build pretrained Xception & InceptionV3 model with input shape (299, 299, 3)
model_xception = build_pretrained_model(Xception, SHAPE_299, xception.preprocess_input)
model_inceptionv3 = build_pretrained_model(InceptionV3, SHAPE_299, inception_v3.preprocess_input)
# Build pretrained NASNetLarge & InceptionResNetV2 model with input shape (331, 331, 3)
model_nasnet = build_pretrained_model(NASNetLarge, SHAPE_331, nasnet.preprocess_input)
model_inception_resnet = build_pretrained_model(InceptionResNetV2, SHAPE_331, inception_resnet_v2.preprocess_input)
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from keras.layers import Concatenate, Dense, Dropout, Flatten, BatchNormalization, Activation
def build_model(pretrained_models, input_shape):
"""
build model with features extracted by pretained models, for predicting testing data.
"""
model_input = Input(shape=input_shape)
extracted_features = [pm(model_input) for pm in pretrained_models]
x = Concatenate()(extracted_features)
x = Dropout(rate=.5, name='dropout')(x)
if x.shape[-1] == 4096:
x = Dense(1024, name='dense1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(rate=.5, name='drop1')(x)
x = Dense(16, name='dense2')(x)
x = Activation('relu')(x)
x = Dropout(rate=.3, name='drop2')(x)
else:
x = Dense(128, name='dense1')(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Dropout(rate=.3, name='drop1')(x)
prob = Dense(1, activation='sigmoid', name='prob')(x)
model = Model(inputs=model_input, outputs=prob)
return model
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# Build model with input shape (299, 299, 3)
model_299 = build_model([model_xception, model_inceptionv3], SHAPE_299)
# Build model with input shape (331, 331, 3)
model_331 = build_model([model_nasnet, model_inception_resnet], SHAPE_331)
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# Summary of Model with input shape (299, 299, 3)
model_299.summary()
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# Summary of Model with input shape (224, 224, 3)
model_331.summary()
Model Visualization¶
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# Show the model graphical representation
from IPython.display import SVG
from keras.utils import plot_model
from keras.utils.vis_utils import model_to_dot
GRAPHFILE='model299.png'
plot_model(model_299, to_file=GRAPHFILE)
SVG(model_to_dot(model_299).create(prog='dot', format='svg'))
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GRAPHFILE='model331.png'
plot_model(model_331, to_file=GRAPHFILE)
SVG(model_to_dot(model_331).create(prog='dot', format='svg'))
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Model training¶
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from keras.optimizers import SGD
# Model compiling
model_299.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer=SGD(momentum=.9, decay=1e-3, nesterov=True))
model_331.compile(loss='binary_crossentropy', metrics=['accuracy'], optimizer=SGD(momentum=.9, decay=1e-3, nesterov=True))
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from keras.callbacks import ModelCheckpoint, EarlyStopping
import gc
# Traininig epochs
EPOCHS = 10
# Files storing model best weights
WEIGHTFILE299 = 'model299.weights.best.hdf5'
WEIGHTFILE331 = 'model331.weights.best.hdf5'
# Model check point
ckp299 = ModelCheckpoint(
filepath=WEIGHTFILE299,
save_best_only=True,
verbose=1
)
ckp331 = ModelCheckpoint(
filepath=WEIGHTFILE331,
save_best_only=True,
verbose=1
)
# Early stopping
es = EarlyStopping(patience=3, verbose=1)
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# Garbage collection
gc.collect()
# Model traininig with image input shape=(299, 299, 3)
history_299 = model_299.fit_generator(
generator_trn_299,
steps_per_epoch=len(df_trn) // BATCH_SIZE_299,
epochs=EPOCHS,
callbacks=[ckp299, es],
validation_data=generator_val_299,
validation_steps=len(df_val) // BATCH_SIZE_299,
verbose=1
)
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# Load the best weight gained from traninig
model_299.load_weights(WEIGHTFILE299)
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# Garbage collection
gc.collect()
# Model traininig with image input shape=(331, 331, 3)
history_331 = model_331.fit_generator(
generator_trn_331,
steps_per_epoch=len(df_trn) // BATCH_SIZE_331,
epochs=EPOCHS,
callbacks=[ckp331, es],
validation_data=generator_val_331,
validation_steps=len(df_val) // BATCH_SIZE_331,
verbose=1
)
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# Load the best weight gained from traninig
model_331.load_weights(WEIGHTFILE331)
Training History Visualization¶
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# Visualization of accuracy&loss for training step&validation step
def view_acc_and_loss(his):
fig, axes = plt.subplots(1, 2, figsize=(18,5))
for i, c in enumerate(['acc', 'loss']):
axes[i].plot(his[c], label=f'Training {c}')
axes[i].plot(his[f'val_{c}'], label=f'Validation {c}')
axes[i].set_xlabel('epoch')
axes[i].set_ylabel(c);
axes[i].legend()
axes[i].set_title(f'Training and Validation {c}')
plt.grid()
plt.show()
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# Plotting loss and accuracy for model_299
view_acc_and_loss(history_299.history)
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# Plotting loss and accuracy for model_331(SGD with lr=9e-3)
view_acc_and_loss(history_331.history)
Model Prediction¶
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# Model_299 prediction
pred_299 = model_299.predict_generator(generator_tst_299, steps=np.ceil(len(df_tst) / BATCH_SIZE_299))
# Clip into range (0.005, 0.995), and reduce to 1 dimension
prob_299 = pred_299.clip(min=0.005, max=0.995).ravel()
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# Model_331 prediction
pred_331 = model_331.predict_generator(generator_tst_331, steps=np.ceil(len(df_tst) / BATCH_SIZE_331))
# Clip into range (0.005, 0.995), and reduce to 1 dimension
prob_331 = pred_331.clip(min=0.005, max=0.995).ravel()
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# Performance on testing set by model_299
show_images_from_df(df_tst, 6, 6, directory='test/', is_train=False, pred_label=prob_299)
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# Performance on testing set by model_331
show_images_from_df(df_tst, 6, 6, directory=FOLDER_TEST, is_train=False, pred_label=prob_331)
Final Result¶
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# Generate submission file for model_299
submission_df = pd.read_csv('sample_submission.csv')
for idx, fname in enumerate(fnames_tst):
idx_df = int(fname.split('.')[0]) - 1
submission_df.at[idx_df, 'label'] = prob_299[idx]
SUBMITFILE_299 = 'submission_299.csv'
submission_df.to_csv(SUBMITFILE_299, index=False)
submission_df.head()
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# Generate submission file fof model_331
submission_df = pd.read_csv('sample_submission.csv')
for idx, fname in enumerate(fnames_tst):
idx_df = int(fname.split('.')[0]) - 1
submission_df.at[idx_df, 'label'] = prob_331[idx]
SUBMITFILE_331= 'submission_331.csv'
submission_df.to_csv(SUBMITFILE_331, index=False)
submission_df.head()
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